Robust Adaptive Feedback Linearization Control Using Online Neural-Network Estimators for Uncertain Linear Induction Motor Drive System

Abstract

This paper proposes a robust adaptive feedback linearization control (RAFLC) using Takagi-Sugeno-Kang (TSK)-type recurrent Petri fuzzy-neural-network (T-RPFNN) to obtain better dynamic and steady-state performance for the linear induction motor (LIM) drive system. The proposed control method includes a feedback linearization controller (FLC), a T-RPFNN estimators and an adaptive PI controller. In the RAFLC design, the FLC is used to stabilize the LIM drive system and the T-RPFNN estimators are utilized to approximate the nonlinear functions of the LIM and the gains of the adaptive controller. Besides, the adaptive PI controller is used to keep the control magnitude bounded and to reduce the chattering in the control inputs. Moreover, the Lyapunov stability analysis are used to drive the online adaptive control laws, hence the stability of the RAFLC scheme can be guaranteed. The dynamic behavior of LIM drive system using the proposed RAFLC not only assures the closed-loop stability, but also guarantees the robust performance for the overall system. An experimental setup is built to check the validity of the proposed RAFLC scheme. The experimental results endorse the proposed RAFLC robustness even at uncertain dynamics existence and external disturbances.